A Statistical Model for Joint Multi-Subject Analysis
Statistical Machine Transliteration with Multi to Multi Joint Source Channel Model
... distortion model, one of the major components in a stan- dard PBSMT system, is redundant for transliter- ...unnecessary model expands the search space and makes it more difficult to find the good ...
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Development of Multi-model for Cogeneration System Using Statistical Analysis
... the statistical model of generating ...Then, statistical models are built in different operating ...developed model as a reference and determine the operating operation of cogeneration ...
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A joint inference of deep case analysis and zero subject generation for Japanese to English statistical machine translation
... a subject chunk just before its ...the subject is omitted, we generate a subject with our deep parser and insert it to a subject position in the source ...
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Multi-subject joint parcellation detection estimation in functional MRI
... the model to infer the brain parcellation. However, so far, the JPDE model has not been used for multi-subject fMRI ...JDE model has been used in a multi-subject context ...
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Comparing Visual and Statistical Analysis in Single- Subject Studies
... ITSA model was determined based on four parameters: level, slope, change in slope and change in ...average model; AR 1 = autoregressive term 1; Level = intercept; Error σ = standard error estimate; Slope = ...
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Statistical shape analysis of Multi-Object complexes
... of statistical shape analysis is the discrimination between populations of objects, exploring group differences in morphology not explained by standard volumetric ...quire analysis of objects in ...
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Interdependent and Multi-Subject Privacy: Threats, Analysis and Protection
... game-theoretic model with real-user parameters in this setting is also ...the model is straightforward and simply increases the number of possible strategies at times where players are not ...
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A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data
... the analysis of task-related brain activity in multi-subject fMRI experi- ...“group analysis” approaches tradition- ally considered in the fMRI literature, which separate the inference on the ...
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A group model for stable multi-subject ICA on fmri datasets
... our analysis of the software packages. GIFT groups individual subject datasets and performs successive PCAs and ...correlation analysis on the individual subject ...
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Multi-scale statistical analysis of coronal solar activity
... , (12) where the fact was used again that q y vanishes at y = 0, L and h . i xy is the double average over x and y. Equation (11) was used to express q x at x = 0. Using these estimates, Fig. 15 shows the averaged heat ...
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Statistical Modeling and Performance Analysis of Multi-Scale Traffic
... queueing analysis techniques like M/M/1, M/G/1, even G/G/1 does not help much ...asymptotic analysis, especially the technique of large deviations [5] ...queueing analysis taking into account the ...
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BeamSeg: A Joint Model for Multi Document Segmentation and Topic Identification
... Comparing BeamSeg’s results to the baseline, we see that in the Biography dataset MultiSeg per- forms better by a 0.12 margin. The main dif- ference between the segmentation of the two ap- proaches is that BeamSeg ...
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A Distributional Analysis of a Lexicalized Statistical Parsing Model
... the subject of recent dis- cussion (Lafferty et ...top-down model gen- erates at its highest point in the tree—influences an attachment ...the model is generating a preterminal, and are thus of ...
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Template Matching using Statistical Model and Parametric Template for Multi Template
... using statistical model and parametric template for ...the statistical model created by principal component analysis method (PCA) can be used to synthesize ...and multi-template ...
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Joint Emotion Analysis via Multi-task Gaussian Processes
... Figure 3 shows the scores obtained. As ex- pected, for smaller datasets the single-task mod- els are outperformed by ICM, but their perfor- mance become equivalent as the training set size increases. SVM performance ...
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Statistical Methods for Integrated Cancer Genomic Data Using a Joint Latent Variable Model
... interaction analysis: γ estimates and 95% bootstrap confidence intervals # feat Genomic effect (γ 1 ) Covariate effect (γ 2 ) Interaction effect (γ 3 ) RNA 1039 ...
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STATISTICAL ANALYSIS OF THE TM- MODEL VIA BAYESIAN APPROACH
... Thurstone-Mosteller model is defined and discussed in section 2. The notations for the model are given in section 3 with the likelihood function of the ...Bayesian analysis of the model using ...
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Joint Sentence Document Model for Manifesto Text Analysis
... a joint sentence–document model to classify manifesto sentences into one out of 57 categories and also quantify the document-level RILE ...The joint formulation is employed not only to capture the ...
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Joint Entropy Analysis Model for DDoS Attack Detection
... flow model. We aim at analyzing the impact of anomalies on the statistical traffic characteristics and to bring to evidence the traffic characteristic signals containing legitimate and illegitimate ...the ...
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In Vivo Evaluation of the Subject-Specific Finite Element Model for Knee Joint Cartilage Contact Area
... board, subject-specific data was used to develop the FE model and in vivo validation ...male subject without a history of knee injury or patellar subluxation participated in this study (34 years, 178 ...
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